DISPERSIVE LIQUID-LIQUID MICROEXTRACTION BASED ON SOLIDIFICATION OF FLOATING ORGANIC DROPLET TECHNIQUE COMBINED WITH GAS CHROMATOGRAPHY-MASS SPECTROMETRY FOR PBDES DETERMINATION IN SEDIMENT SAMPLES

NERINA B. LANA; BERTON, P.; NÉSTOR F. CIOCCO; JORGELINA C. ALTAMIRANO

Bruselas

Congreso; Dioxinas 2011; 2011

Polybrominated diphenyl ethers (PBDEs) have been used extensively over the past two decades as additive flame retardants (FRs) in most types of polymers to prevent ignition and to slow the initial phase of combustion. On the other hand, PBDEs are considered persistent organic pollutants because of their ubiquity, persistence and accumulation in the environment. Its harmful effects on human health and the environment, has led to its inclusion of the Stockholm Convention in 2009. In the past few years new extraction techniques, especially in the microextraction category, have gained interest for PBDEs determination in biological and environmental samples. Efforts have been placed on the miniaturization of the liquid-liquid extraction procedure by greatly reducing the required organic solvent amount. In this way dispersive L-L microextraction and further solidification of floating organic droplet (DLLME-SFO) technique has been developed and proposed as a new analytical approach for extracting, cleaning up and preconcentrating polybrominated diphenyl ethers (PBDEs) from sediment samples prior gas chromatography-tandem mass spectrometry (GC-MS/MS) analysis. Statistical analysis was used to evaluate the significance of the microextraction factors, and determine which combination leads to the optimum results. The combination of microextraction and chemometrics tools significantly simplify sample processing, and addresses problems related to improvement in detectability and method validation6. In the present work, the study and optimization of the DLLME-SFO procedure for determination of PBDEs in sediment samples by GC-MS/MS was carried out through a multivariate approach by using 2k-1 factorial and response-surface designs4. Desirability function was used to optimize the multiple response criteria based on analytes? peak areas4.